Shownotes
In this episode of Investing Principles, Andrew and Larry Swedroe discuss Larry’s new book, Enrich Your Future: The Keys to Successful Investing. In this series, they discuss Chapter 1: The Determinants of the Risk and Return of Stocks and Bonds.
LEARNING: Look for key metrics, traits, or characteristics that help them identify stocks that will outperform the market.
“Intelligent people maintain open minds when it comes to new ideas. And they change strategies when there is compelling evidence demonstrating the ‘conventional wisdom’ is wrong.”
Larry Swedroe
In this episode of Investing Principles, Andrew and Larry Swedroe discuss Larry’s new book, Enrich Your Future: The Keys to Successful Investing. The book is a collection of stories Larry has developed over the 30+ years he’s been trying to help investors. Larry is the head of financial and economic research at Buckingham Wealth Partners. You can learn more about Larry’s Worst Investment Ever story on Ep645: Beware of Idiosyncratic Risks.
Larry deeply understands the world of academic research and investing, especially risk. Today, Andrew and Larry discuss Chapter 1: The Determinants of the Risk and Return of Stocks and Bonds.
Chapter 1: The Determinants of the Risk and Return of Stocks and Bonds
In this chapter, Larry looks at research that revolutionized how people think about investing and how to build a winning portfolio. The goal is to help investors learn how to look for key metrics, traits, or characteristics that help them identify stocks that will outperform the market, at least in terms of delivering higher returns, not necessarily higher risk-adjusted returns.
The three-factor model
The first research Larry talks about is by Eugene Fama and Kenneth French. Their paper “The Cross-Section of Expected Stock Returns” in The Journal of Finance focused on research that produced what has become known as the three-factor model. A factor is a common trait or characteristic of a stock or bond. The three factors explained by Fama and French are:
- Market beta (the return of the market minus the return on one-month Treasury bills)
- Size (the return on small stocks minus the return on large stocks)
- Value (the return on value stocks minus the return on growth stocks).
The model can explain more than 90% of the variation of returns of diversified US equity portfolios. The research shows that ensemble funds are superior to individual funds. It’s better to have a multi-factor portfolio. So you could own, say, five different funds that have exposure to each individual factor, or you own one fund that gives you exposure to all those factors. The ensemble strategies always tend to do better.
The two-factor model
Larry also highlights a second model by professors Fama and French, the two-factor model that explains the variation of returns of fixed-income portfolios. The two risk factors are term and default (credit risk). According to the model, the longer the term to maturity, the greater the risk; the lower the credit rating, the greater the risk. Markets compensate investors for taking risks with higher expected returns. As with equities, individual security selection and market timing do not play a significant role in explaining returns of fixed-income portfolios and thus should not be expected to add value.
Buffett’s Alpha
Another significant academic research publication is the study “Buffett’s Alpha.” The authors, Andrea Frazzini, David Kabiller, and Lasse Pedersen, examined the performance of the stocks owned by legendary investor Warren Buffett’s Berkshire Hathaway. They found that, besides benefiting from using cheap leverage provided by Berkshire’s insurance operations, Buffett buys safe, cheap, high-quality, and large stocks. Their most interesting finding was that stocks with these characteristics tend to perform well in general, not just the stocks with these characteristics that Buffett buys. Larry observes that Buffett’s strategy, or exposure to factors, explains his success, not his stock-picking skills. Also, he never engages in panicked selling.
Larry says that investors don’t need to be stock pickers like Warren Buffett. They can simply buy stocks with the same characteristics as Warren Buffett’s stocks without doing all the research. Today, companies like AQR, Avantis, Bridgeway, Dimensional, and others use that research so that every investor can access those characteristics and decide which characteristics they want to invest in. The iShares MSCI USA Quality Factor ETF (QUAL) buys quality stocks. It has an expense ratio of just 0.15% and is highly tax-efficient as an ETF.
Luck versus skill
Academic research has demonstrated that efforts to outperform the market by either security selection or timing are improbable in proving productive after taking into account the costs, including taxes, of the efforts. For example, studies such as the “Luck versus Skill in the Cross-Section of Mutual Fund Returns” have found that fewer active managers (about 2%) can outperform their three-factor-model benchmark than would be expected by chance. That is even before considering the impact of taxes, which for taxable investors is typically the most significant expense of active management (greater than the fund’s expense ratio and/or trading costs).
Larry, therefore, recommends:
- Developing a portfolio that reflects your unique ability, willingness, and need to take risks. The equity portion should be globally diversified across multiple asset classes. The fixed-income portion should be diversified in terms of credit and term risk, as appropriate.
- Avoiding the use of actively managed funds. Instead, invest in funds that provide systematic exposure to the factors you seek exposure to, such as low-risk and tax-efficient index funds.
- In the case of fixed-income assets (for those individuals who have sufficient assets to do so), build a portfolio of individual Treasury securities and/or FDIC-insured CDs, and for taxable accounts, AAA- and AA-rated municipal bonds that are also either general obligation or essential service revenue bonds. Doing so dramatically reduces the credit risk and, therefore, the need for diversification (which is the benefit of a mutual fund).
- Having the discipline to stay the course, ignoring the noise of the markets and the emotions caused by the noise—emotions that cause investors to abandon even the most well-developed plans.
Notes
- Michael Lewis, Moneyball (Norton 2003), p. 67.
- Eugene Fama and Kenneth French, “The Cross-Section of Expected Stock Returns,” The Journal of Finance (June 1992).
- Andrea Frazzini, David Kabiller and Lasse Pedersen, “Buffett’s Alpha,” Financial Analysts Journal (September 2018).
- Eugene Fama and Kenneth French, “Luck versus Skill in the Cross-Section of Mutual Fund Returns,” The Journal of Finance (September 2010).
About Larry Swedroe
Larry Swedroe is head of financial and economic research at Buckingham Wealth Partners. Since joining the firm in 1996, Larry has spent his time, talent, and energy educating investors on the benefits of evidence-based investing with an enthusiasm few can match.
Larry was among the first authors to publish a book that explained the science of investing in layman’s terms, “The Only Guide to a Winning Investment Strategy You’ll Ever Need.” He has authored or co-authored 18 books.
Larry’s dedication to helping others has made him a sought-after national speaker. He has made appearances on national television on various outlets.
Larry is a prolific writer, regularly contributing to multiple outlets, including AlphaArchitect, Advisor Perspectives, and Wealth Management.
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Transcripts
Andrew Stotz:
Fellow risk takers, this is your worst podcast host from a Andrew Stotz from C, I'm the worst. I've already messed it up from a Stotz Academy. And today, I'm continuing my discussion with Larry swedroe. And Larry and I are going to be talking about his latest book enrich your future, the keys to successful investing. The book comes in a few parts, four parts. The first part is how markets work. And we're going to be discussing Chapter One, which is the determinants of risk and returns of stocks and bonds. Larry, take it away. And tell us a little bit about this book. You know why you wrote it, what people get from it. And then let's talk about that first chapter.
Larry Swedroe:
Yeah, so this is really sort of a capstone book for me, if you're well, it's a collection of stories that I've developed over the 30 years or so that I've been trying to help investors, stories, I've learned a great way, the best way to help people learn difficult concepts. Because if you could teach them an analogy that's related to cooking, or gardening, or movies, or sports, as we'll discuss now, and they understand that in that venue, you can then apply it to investing and they'll say, Aha, the light bulb goes on. And, as you will probably have that mentioned before, in previous discussions, I was taught early on, if you tell somebody a fact they learn, if you tell them the truth, they'll believe. But if you tell them a story, it will live in your heart forever. So to me, this is probably the book I'm the most proud of, or it's at least my favorite book, because it's a collection of all the wisdom that I've gathered in the 30 years. So
Andrew Stotz:
yeah, maybe, maybe, maybe I'll explain what I like about this book just for the readers out there. And I'll have a link to it in the show notes so you can get it. But what I really like about this book is that I know because it's your capstone, and you know, you're really covering the core principles that when you refer to research in this book, you're really referring to pretty seminal research, not just you know, I don't need to look at 100 different academic research papers. But in this book, I get a touch of maybe 50 great academic papers not explained in an academic way, but explained through a story. And for me, I love to go into the papers. So that's fascinating for me. So that's what I like.
Larry Swedroe:
Thank you, Andrew. And I think that's what gives my book power, that you have the story, it now makes sense. But you could be fooled by statistics and math pretty easily. So what I do then is provide the empirical research to support the story. So now you have the truth in the data, right, and to backup the story. So the first story talks about, as you mentioned, the determinants of the risk and return of stocks and bonds. And I sought a way to try to explain this in a simple way. And I came up with the analogy to now a famous individual fellow named Bill James, who most people probably never heard of. But now anyone who's involved in sports and gambling related to sports, will know Bill, James name. In 1977. James did a self published book, The 1977, baseball abstract, and 75 people bought the book. Right today, his version of that book is called The Bill James handbook. It's the basis for, you know, the movie called Moneyball and the book. Michael Lewis wrote about that, which explained that the his research found that in baseball, people vastly overrated batting averages and homeruns. Okay, they were not the most important determinants of who is the most valuable player for the team, at least when it came to hitting. And James found that you got a much better result in predicting the winners, if you will, of the better hitters who contributed to the teams by looking at what not only your batting average, but your on base percentage, taking into account walks and your slugging percentages, not just home run, so doubles and triples also matter. And so they came up with this overall statistic. And that's how the Oakland A's with a very low payroll were able to build championship quality teams, they just were a lot smarter than everybody. And then of course, everyone figured out, you could copy that. And now every baseball team has a status decision. Today. They're called saber magicians. And it's true in every sport now, they find the key traits or characteristics, and not just what's easily visible to the eye, like a batting average, or maybe a field goal percentage in basketball. So now the question is, what is this all have to do with investing? Right, we understand what we want to look for are key metrics, traits or characteristics that help us identify stocks that will outperform the market, at least in terms of delivering higher returns, not necessarily higher risk adjusted returns, but a higher returns, okay? Because you can own the lock it, you don't have to do anything, right, just own a total market index fund, which is a perfectly good way to invest. Okay, so what we got is a series of academic research papers that came out beginning in the 1960s. The first asset pricing model was called the cap M. And that was a single factor model. Just like batting average only here, it's now market beta. You know, if you have stocks that are much more volatile in the market, then the theory was you should be rewarded for that risk. So you had a beta of more than one, and you should have a higher expected return for that. And if you're a more defensive stock, like a grocery store chain that wasn't so susceptible to the economic ups and downs, your beta was less than one. So you should expect lower returns for the less risk doesn't mean high beta stocks were good investments, and low beta stocks or bad invest. But that was the working model up until the late 1980s, early 90s, when academic research began to come out showing that cheap stocks or value companies have higher returns than expensive or growth stocks. And smaller companies have higher returns than larger companies. So you could think of them as the equivalent of you know, like the cap and market beta was batting average. Now size was walks and value was slugging percentage. We have now three traits that help determine the outcomes of portfolios. And then further research came around 1994 With the addition of momentum from Jagadish and Tippmann, wrote a paper so momentum helped explain returns and then Robert Novy Marx wrote a paper in 2013, adding profitability. So they found interestingly enough, that more profitable companies produce higher returns than less ones. And then we add kind of a capstone of all of this. A team from AQR basically, if you will, reverse engineered Warren Buffett's great returns, did the research and said Are they a traits that we can identify that if we could buy stocks that had the same characteristics of the stocks that Warren Buffett bought, then we don't have to be a stock picker, like Warren Buffett, we could just buy an index of stocks with those traits. And that became the quality factor. And that takes, you know, companies that are not only cheap, but they have low volatility of earnings, low operating leverage, low financial leverage, and what's called Low idiosyncratic risk to you know, versus the market. And once you look at those characteristics, they found that Buffett, his Alpha was no longer statistically significant, you could have gotten the same returns, not counting his use of leverage from his reinsurance companies. As he did the stock Seong you could evolve and today companies like AQR Avantis Bridgeway dimensional and others of Blackrock use that research so that every investor can access those characteristics. So, that gives investors a big edge, they can decide which characteristics they want to invest in, follow the research should they choose to do so. And the same thing is true or not Just on the equity side, fama and French wrote a paper showing that there were two traits that really determined all of bond returns. And they were the maturity or term risk, okay? And also the credit quality. And that's it. So in other words, if you buy triple B bonds, okay, you're gonna get that index return versus a mutual fund, or ETF that buys only a certain group of triple B, because they're smarter than the market, they think. And the answer is that doesn't work. The vast majority of bond funds, even more so than stocks, underperform their pure benchmarks. So that's the basis of our story. You don't have to do any research. All you have to know is what the academic research says, Here are the key characteristics. Andrew Burton, and I wrote a book your complete guide to factor investing. And we show what are the key five equity factors, and what are the two bond factors, and you can invest that way. And we've been given our books, the mutual funds, we think are the best at giving you access to. So let's
Andrew Stotz:
go over a couple quick things on that. The first thing I want to highlight for those that are following along in the book, the reason why you should buy the book is because Larry has given us a list of single style funds that are domestic that are exposed to different betas, you know, market beta, exposed to small value, he's got multi style funds, all kinds of different ETFs, and funds that you're highlighting in there, that are a great place to start doing your research. And, of course, none of this is investment advice. It's really about research. So that's the first thing that I really appreciated in the back of the book was that we could operationalize it. The second thing is, there's three key three academic papers, I just want to highlight that I'll put a link to in the show notes. The first one is comes out in 1992 Nights fama and French is cross section of expected stock returns where they came up with their three factor model. And then after that, the 2010 fama French one, which was called luck versus skill in the cross section of mutual fund returns. And then the third one was Buffett's alpha, which describes Buffett's alpha in the financial analysts journal. Any comments on that as the flow of research, they're particularly the fama French stuff?
Larry Swedroe:
Yeah, so fama and French are often given credit for discovering those sides of value factors. They never claimed any such thing. They didn't discover it. In fact, they summarized research. Ralph Benz is the one who uncovered the size effect. And a bunch of papers were written on cheap companies or value stocks outperforming, but they get credit for turning it into a model that could be used a new asset pricing model that became the workhorse model for the next 20 or so years, until the profitability and quality and momentum were really added. So that's one thing. Second thing on the Skill versus lock, what fama and French did is looked at the statistical evidence to see if more active managers are outperforming than would be purely expected randomly. Just like if you put 10,000 people in a stadium and ask them to flip a coin heads and tails, somebody at the end of the day will have flipped 15 heads in a row. Now, we know that's not scale, and you wouldn't bet on that person to win the next coin flipping contest. But when it comes to investing, investors don't think that way. But there are 10,000 mutual funds randomly, you should expect some will outperform. And what fama and French found was that less than 2% of all actively managed funds were generating statistically significant alpha, even before taxes, after taxes, it was probably 1%. But that was less than what you would expect purely randomly. And other papers since then, have replicated that performance. And the third one is you said, you know, we now every one of the mutual funds that I own, or ETFs incorporate all of these things that Warren Buffett had been telling people for 60 years. Here's the kind of companies I bought, it wasn't a secret fit to actually just tuck AQR team to reverse engineer it, but they someone should have been doing that probably 50 years. So Buffett was never a great stock picker, when he deserves tremendous credit for is identified. Find these key traits are he found the equivalent of walks and slugging percentage 50 years before the academics that?
Andrew Stotz:
Yeah, that's a and also, you've taught us before in prior episodes, to use the software portfolio visualizer. And, and we can assess Buffett's performance. And I've used that in my classes by showing that in the last 20 years, I asked students, do you think he outperformed outperformed by a lot underperformed or performing in line? And what you find is that Buffett basically performed in line over the last, you know, maybe even a little bit less, it just depends on when you pick the exact date. But for 20 years, he hasn't really outperformed. Of course, that doesn't mean he doesn't end up with the most amount of money, because he's allowing them capital to continue to compound. But I think that's an eye opener, that's telling us that these factors that were obscure factors, maybe originally, like, for instance, a good example of an obscure factor for the listeners or viewers out there is calculating the number of shares outstanding. And maybe by looking at the number of shares outstanding of a company, like how often do they increase or not increase? But you know, that you could find that that you know, that's an obscure factor that may lead to outperformance if a company doesn't increase their shares outstanding, but it runs into another issue, which is that, well, that just may be a, it may be capturing the fact that the company has a high free cash flow yield, and they're able to, so it may actually be a factor that captures profitability. And that's where I wanted to go back also to the three factor model as a step where we look at market beta and value and size and say how much of the outperformance or performance can be captured by those three factors alone before we add in, let's say momentum or profitability.
Larry Swedroe:
So here's what the research found. When shop and others created that cap and model. Right away, they knew what was wrong. First of all, all models, by definition are wrong, they'd be called laws, like we have in physic physics, there are hypotheses, right? And they give you a picture of the world, right? But it's not an exact replica. And they found that the cap n only explained about two thirds of the variation of returns about among diversified portfolios. So it gave us the first step, just
Andrew Stotz:
so just to be clear, for the listeners out there. When you say the cap M, you're saying the market, the one factor of market beta, one
Larry Swedroe:
factor market beta, so let's just use an example to help the listeners, let's say the stock market went up 10%, and you had a market beta of 30 of 1.3. So you're 30% more volatile. That means you should have gone up 13%. If you went up 12, you had a negative alpha of minus one, okay. And they found that this wasn't as good a predictor as they thought. And they started to find anomalies like smaller companies, and value companies. And so the cap M if you had one fun returning 13 and the other 10, the cap M probably explained 2% of that 3% difference, but the 1% was left unexplained by the khalfan fama and French braid the refactor model and the explanatory power or the R square. There went up to like 92%. That's a huge advance, and it tells you there's not much more left. Now, still, academic research went on and momentum and prove that another couple of percent and profitability improved it even further. So, you know, you're talking about left with very little room to add value. And yet active managers have that add a lot of value to overcome their expenses. And there's not much room more room left. That's why it keeps getting harder and harder for active managers to outperform. Okay, because what was once a source of alpha, I could just buy value stocks and play Mafia like Warren Buffett did, and he was right to do so because it wasn't in the model. But once it's in the model, you can't claim it anymore, because Andrew Stotz can go online. So I want to find a fund that has a high loading on value and quality and size and it's Avantis fund or the Bridgeway fund or the DFA fund, and they're all slightly different versions. Okay in this, and you will capture those premiums. Here's another one that's a brand new paper, which I just wrote up. And so it's just I mentioned it only to show that the research is ongoing, because the rewards are great if you could find something right. And a paper proposes a very interesting thing said there really is no size effect. What there really is, is a merger and acquisition effect. So company, if you could find a way to identify the stocks that are most likely to be acquired, you would capture the size premium. And the other stocks don't have a size premium. It's this small group of stocks that get acquired and then their prices go way up. And they identified some characteristics of companies, they found that Bill James, you know, slugging percentage and batting average and walks and stuff on base percentage, and things like that, and you could add stolen bases, and you know, it's stuff. And that's what they found. And it's an interesting paper, I wrote it up. And they found basically, it's kind of the companies that are profitable, that generating cash and throwing it off, right. And so they're actually already in those funds. So I don't think there's anything greatly new. Okay. But it's interesting that, you know, that research and so we shouldn't be shocked, with all the computer power and all the high reward for generating, you know, a little bit of extra alpha, we'll likely to continue to see new research. And that gives me a lot of fun, because I love to read the research and learn something new almost every day.
Andrew Stotz:
Now, Larry, I feel like one of the reasons why we all should be, you know, listening and talking about this with us, because we learn a lot. I did a little research that I want to share to show some calculations, because this was the farm of French. Mainly, the two reports were for farmer French, the research that you talked about in this section, I'm going to turn off my video for a second and I'm going to share my screen and I'm going to show you some research that I just pulled off the internet. Basically, I went to Kenneth French's site. And here, I believe you can see this on the screen. Can you see the Yep. Okay, I say and what I thought this would be a good way to help us all understand what you're talking about. And so the first we talked about the market, the market, what market premium? Is that what you call it, I just wrote down the market. And, and what we can see this market beta market beta. And what we can see is from 1964 to 2023, it was 7.8. But now he's
Larry Swedroe:
just so everyone understands that, Andrew, what's the market return less the rate of return that Rf is the risk free rate, which is one month treasury bills,
Andrew Stotz:
right, got it. So in other words, you get, you get additional compensation, for taking additional risk by taking your money out of a risk free bond and putting it in the stock market. Now, the interesting thing is we can see from 2014 to 2023, that that went up to 11.5, which kind of gives you a picture of how just very strong the market has been versus very, very low risk free rate interest rates during that period. Now, the second factor is the small factor, which we can now see if we look at it over the period of 1964 to 2023. It's only 2%. Now, and what's fascinating is now it's actually from the period of 2014 to 2023. It's pretty much gone, and it was a negative 2.7%. So that I think support some of what you're saying is that it's been exploited.
Larry Swedroe:
You can't draw that conclusion. It's certainly possible. But that shouldn't make it go negative. What what you're seeing here is that over long periods of time, various factors because of regime changes, or something different in the economy, it just means that there is a random period where small stocks did very poorly, and that tends to occur after periods when it does very well,
Andrew Stotz:
because what you would say is out of favor.
Larry Swedroe:
So it could be they're out of favor. Now I will make the case, however, that there has been a massive change, really since 2002. In the markets because of Sarbanes Oxley, a US law which made it much more expensive to go public. And today small stocks. There are made up the market of small stocks is much different in its characteristics than it was 2030 years ago. Today, something like 40% of the stocks, and the Russell 2000 lose money in really weak companies. And so if you just look at small and don't screen out those garbage lottery like stocks that the academics have shown, you shouldn't buy, but retail investors tend to love. And that leads those to be overpriced, you can save the size premium by saying I'm only going to buy small companies that are cheap and profitable, as well. And all of a sudden, you get much better returns. Okay, fantastic. So some periods. Andrew, where lodge does better for a decade, and then small does better. And we know it's unpredictable, you cannot identify them ahead of time, because otherwise active managers would persistently outperform, and there's no evidence that they could do it.
Andrew Stotz:
Fantastic. Now let's look at value because that's the third of the three factor model original. And you can see in this from 1964 to 2023, it says 3.8%. So maybe you can explain that 3.8%. And any observations you'd make from the data from, from Ken French,
Larry Swedroe:
the first thing you have to remember is all these factors are long, short portfolio, so they're not investable, and like you would in a long only mutual fund. As I mentioned, the market or beta, as it's referred to is the market return minus the risk free rate the small premium is is small minus big. So return on small stocks minus the return on large stocks. Value is the return on Hi, I booked a market so they're selling cheap, there's a lot of book value relative to the market minus the return on low book to market or growth stocks. Profitability RM W stands for the return on companies with robust profitability minus returns of those with weak profitability. And CMA is investment. So return on companies that are conservative on their investment, minus the returns on companies that are aggressive in investment. So that's how the factors work. And what the research also shows, is that a multi factor portfolio as opposed to a portfolio that owns each of the factors, so you could own say, five different funds that have exposure to each individual factors. Or you could own one fun, that gives you exposure to all those factors. And the ensemble strategies always tend to do better. Okay, I want to talk about 80 of reasons. So that's a summary of the reason. That's great.
Andrew Stotz:
Also, I tried to do I did a 10 year moving average of the various factors I did 10 years because the chart would just be too busy. If it was one year, three years, five years. And there's some interesting observations here, such as the small cap premium right here peaked in about 1983. And then we had, you know, a pretty pretty up and down on that. And then we can also see the market premium during times of boom periods. That's rising. Any any observations you would make from this? Yeah,
Larry Swedroe:
this is really important because investors make huge mistakes. If Andrew, if you could point your point there at that first period with small stocks did great and peeked in around 83. Well, why did the return do so poorly because investors were chasing, they were wanted those great returns, small stocks are gotten and the P e is a small stocks went through the roof, which by definition, virtually Doom them to very poor returns going forward. And the same thing happened to the stock market. If you look at your red line, it peaks up around 99 Why were very great returns and that drove the P E ratios up to about 40 with the.com era guaranteed that the next 10 or 20 years for the really poor and then you had in oh eight you have very low prices and they go up now you don't want to be chasing again. There. So one thing you should take from this is very high valuations can come not because the companies are profitable, but because people have just bid up their prices and then not justified by The earnings are growing that fast. The other is that there's a lot of randomness in these movements. And you're better off building a portfolio that owns some exposure to each of them. Because there are times when value does well, there are times when profitability does well, when investment does well, etc, you're better off building a portfolio as broader exposure to these. And
Andrew Stotz:
I'm areas I'm going to, I'm going to show the last one, which I think I'm going to hold on, let's see if I can find my slides. So I tried to bring all this into text where I show the actual five factor model. And then I tried to describe each of these. And so for I'm going to put this into the show notes so that people can go through it. Maybe Did I make any mistakes here? No,
Larry Swedroe:
you got everything right. Looks good to me. I'll make one interesting observation here. So you think companies that are investing conservatively? Why should that work? Well, it's fits with economic theory, if you have low investment, it should be because you have a high cost of capital, and you investments you want to make just can't clear that hurdle. So if you have a high cost of capital as a company, you're going to be conservative and investing. But that high cost of capital, the flip side of that is a high expected return to the investor, the reverse would be true, if I have a very low cost of capital, like, say, a.com company in the 90s, I'm going to be investing aggressively, because people give me money really cheap. I don't have to give a lot of way, a lot of equity to get that capital. So I'm going to have high investment. But guess what I should if I have a low cost of capital, I should have a low return to the providers of that gap. Now, here's one anomaly. Think about that, about this point. What if you're Google, and you have a high return on capital, but it's the return on your investment is higher than your cost of capital? You should be investing as aggressively as you can, as Nvidia has been doing? Right. So you've got this anomaly, if you will, you're saying I don't want to invest in companies with high asset growth? Because they Larry says, or the CMA says they have low returns, where do you want to avoid doing is investing in companies with high asset growth, that aren't profitable enough to cover there plus the capital. And so you can screen those companies out that have that high asset growth, and just don't buy them and limit yourself to companies that meet the other criteria. And, you know, and then you get them cheap. So if you look at the research, it clearly shows that companies and that are small, cheap, and profitable. They are the ones that have by far the highest returns. Even in just looking at profitable companies, there is actually no premium between large cap stocks that have high profits and low profits. But there's a massive difference between small cap stocks that have high profits and low profits. Massive
Andrew Stotz:
low profits are much lower or punished
Larry Swedroe:
more their returns have been about 8%. But the small, cheap, profitable companies. So you want to in the Lord's? I don't think I said exactly right. So I'm going to repeat in the large companies that are cheap, and profitable. They're no different return than large companies that are expensive and profitable. They all returned about eight and a half percent. But if you bought the small companies that were cheap, and profitable, you got like 21%, where if you bought the small companies that were expensive and profitable, you got a so I really liked to stick with that smaller asset class, but by the other factors as well, because the the premiums for value, momentum, investment, profitability, have all been much higher in small stocks and a lot stocks, but you have to be prepared. There are regimes where it could be 10 years where they don't do so well. So you got to have the discipline to stay the course. So I want
Andrew Stotz:
to run I put up with the some actionable advice. That is for the absolute beginner, you've talked about the idea of just buying a market, ETF or market fund that owns every stock in the market. And the good news for and correct me if I'm wrong, but the good news for that person is you're actually going to be exposed to those different factors. It's just that the weighting of the small cap factor, for instance, in that is very tiny. So correct me when I'm wrong
Larry Swedroe:
there. Yeah, Andrew. So you have to remember that by definition, construction, remember, these are long short portfolios. So if you want small stocks, which gives you positive exposure to the size effect, but you're also long lat stocks, which gives you negative exposure to the size effect, it nets by definition to zero. If you own the total market, you have positive exposure to value, because there are value stocks in the market. But you also have negative exposure to value because you want growth stocks, it averages to zero. And the same thing. So if you want exposure to those factors, not the stocks, but the factors, then you have to tilt or overweight in your portfolio. So if the market is 20% value stocks, you have to have 3040 50%. But
Andrew Stotz:
we've never met one, let me try to those. Let me try to simplify that if I can, before we get to including it in your portfolio, let's talk about the construction of the ETFs, or the funds that you've talked about, for instance, in the back of your book, you've just made an important distinction if let's just say that if we if we see that the value factor is is a valid factor, let's say you can gain from buying a group a large group of cheap stocks. And you would gain if you shorted a large group of expensive stocks,
Larry Swedroe:
over the long term was a little less than 4% a year.
Andrew Stotz:
Yeah. And so the result of that is that you as an individual aren't going to get that because you're not going to go short if you're owning that index fund. But the fund providers, the ETF providers that are providing the factor, exposures are doing that long, short, and they're nurturing it or not, am I getting that on? Only
Larry Swedroe:
one case? If you're on a long short factor fund, like AQR does, they have a fun called their style premium fund. It has four styles or factors, like value momentum, what they call defensive, which is a quality and something called the carry trade. So you buy something with a high cash flow, and you short them with low cash flow, let's say so today, you might buy US Treasuries and short Japanese bonds, because they have low yields, right. And they trade these things. So there you are actually long short, if you bite dimensionals, small value fun, you're a long lonely, but you have exposure, if that's your only fun to the market factor, you have exposure to the size factor, and you have exposure to the value factor, and I would urge you to go to portfolio visualizer. And you can see just how much and compare them to say Vanguards funds, a Vanguard small value fund will also have exposure, but it'll be a lot less exposure. For example, their small value fund last I look, market cap was about six and a half billion. Okay. dimensionals was maybe two and a half billion.
Andrew Stotz:
Can I ask a question to get clarity on this. So you've talked about dimensional being ultimately it's a long only exposure to refactor, which most of them it sounds like most ETFs and funds are in fact, long only. And if we look at an index, that is a total market index, you're going to have exposure to that factor within your market index, but it's just going to be a tiny exposure. Let's just say it's small quality, you can have the stocks in your portfolio.
Larry Swedroe:
That's not right, Andrew again, because if you own the total market, the large stocks in your portfolio, Google let's say, gives you a negative loading on the size factor. Then you on this little small company XYZ it gives you positive exposure. So you go through the you know 35 on Did stocks, okay, of which may be, I don't know, 2000 of them are small. And you wait how much in their portfolio and you give it a loading for, you know how much how tiny they are. And then you do the same for the 1000 livestocks in there and your weight and your sum will be zero. So you have no exposure, now do the same thing with dimensional, they have none of the light stocks in their portfolio, they have none of the growth stocks. So when they do that same multiplication, it'll be pure exposure. So it might end up with being say, 75% exposure to the value effect, and 90% exposure to the size of fat. So that's the notch. They don't they're not long, any of the others that would give them negative exposure.
Andrew Stotz:
So this is a great way to learn for the listeners and viewers, because I keep coming up with the wrong answers, and Larry's correcting me. So let me try again and see how I do let's take an individual who has bought a total market index, and then they add in a one factor dimensional fund, let's say, let's just say that that's value. Right? That means that they're tilting their overall market exposure slightly towards value is that would that be correct?
Larry Swedroe:
That's absolutely right. And if they own 50% of their portfolio was total market, and 50% was dimensionals. Fun, let's assume this, the market fund, which you own 50% of has zero exposure to value, the value fund, let's say has 70% Exposure to value, but you only own half of it in your portfolio. So the total portfolio exposure will be point three, five, let's call it 1/3. That will be your exposure there. So it's how much you tell determines your exposure.
Andrew Stotz:
And when we talk about exposure to these factors, you know, a very simplified version for someone that just doesn't want any trouble. They just buy the total market. But let's say for someone that's willing, they don't want to buy individual stocks, but they do want to build some factor exposure portfolio is what they're doing is bringing together three to five different funds or ETFs that are exposed to these different factors and in different weights or equal weights, or how do they do that?
Larry Swedroe:
Yeah, so the easiest way, let's just stick with the US only investor. As I mentioned earlier, ensemble funds are superior to individual funds, I will explain very simply why. Let's say you're a Value Fund, how did you get to be value tends to be the stock prices are falling and they're getting cheap. So now a stock drops it was performing poorly, and you're a Value Fund buys it. You also own a momentum fund. Well, its stock price watching that stock, it's going down it's gonna go short. So now you're paying two fees, and one bought it and one sold, it doesn't make any sense. You got two trading costs, and you're paying two fees. So you want to incorporate it into one. So if I want to own a fund to tell a I'm gonna own a small value profitability quality fund that screens for momentum all in one, and there are fun families I've mentioned the Vantis dimensional Bridgeway AQR these are the funds families that are the leading researchers who employ this academic research. And then you can run them in Portfolio visualizer. And you can see what the loadings are will tell you, and you can see what their returns have been and their alphas because trading matters how effective you are in your fun construction rules how often you rebalance by hold Rangers actually matter. And your definitions matter. So that's what how I determine which of the vehicles but you can't go wrong with any of the funds I've listed in the book, but you should do your own research to learn about these issues. Yep,
Andrew Stotz:
I'm going to put all some of that I'm going to do a little example in the show notes. But in the back of the book in the appendix, you can see things like multi style finds like large and value and profitability and quality or small and value and profitability and quality and some that even blend in momentum and so it's a great a great primer for the Those of us that want to learn so that was a lot, Larry, I really that first chapter is a knockout and it's not that long for the readers out there. I highly recommend you get it on links in the show notes. Make sure that you get it. Is there anything you would add before we wrap up, Larry?
Larry Swedroe:
No, except this is I've written 18 bucks and this is my personal favorite. You could check it out on Amazon last I looked there were 24 reviews 23 of which were five stars and one four stars. So I'm pretty that hopefully tells people a book is worth reading. can read the reviews. And the book is worth reading just for Cliff Asness is brilliant forward.
Andrew Stotz:
Yes, indeed. Well, Larry, I want to thank you again for another great discussion about creating, growing and protecting our wealth for listeners out there. You can follow Larry on Twitter and also on LinkedIn. He's relentless out there. This is your worst podcast hose Andrew Stotz saying. I'll see you on the upside.